UPMC/LIP6 at ImageCLEFannotation 2010

In this paper, we present the LIP6 annotation models for the ImageCLEFannotation 2010 task. We study two methods to train and merge the results of different classifiers in order to improve annotation. In particular, we propose a multiview learning model based on a RankingSVM. We also consider the use of the tags matching the visual concept names to improve the scores predicted by the models. The experiments show the difficulty of merging several classifiers and also the interest to have a robust model able to merge relevant information. Our method using tags always improves the results. Key words: SVM, Multi-Class Multi-Label Image Classification, Imbalanced Class Problem, Semi-Supervised Learning, Transductive Learning, Visual Concepts, Ranking SVM

• Type: Conference
• Year: 2010
• Where: CLEF
• Authors: Ali Fakeri-Tabrizi, Sabrina Tollari, Nicolas Usunier, Massih-Reza Amini, Patrick Gallinari
• Keywords: CLEF 2010, ImageCLEFannotation 2010 Task, Information Technology, Multiview Learning Model, Visual Concepts